import geopandas as gpd
import matplotlib.pyplot as plt
import kagglehub
import pandas as pd

# Descarrega de les dades
path = kagglehub.dataset_download("headsortails/train-stations-in-europe")

print("Arxiu descarregat a:", path)

Arxiu descarregat a: C:\Users\esthe\.cache\kagglehub\datasets\headsortails\train-stations-in-europe\versions\22

# Carrega dades i neteja columnes
df = gpd.read_file(path + "\\train_stations_europe.csv", encoding="utf8")

print('Mida original del dataset:', df.shape)

df = df[pd.to_numeric(df['latitude'], errors='coerce').notnull()]
df = df[pd.to_numeric(df['longitude'], errors='coerce').notnull()]

print('Mida del dataset després de la neteja:', df.shape)
df.head()

Mida original del dataset: (64037, 14)
Mida del dataset després de la neteja: (62142, 14)

# Convertir a GeoDataFrame
gdf = gpd.GeoDataFrame(df, geometry=gpd.points_from_xy(df.longitude, df.latitude), crs="EPSG:4326")

# Pintar
fig, ax = plt.subplots(figsize=(12,20))
gdf.plot(ax=ax, markersize=2, color='blue', alpha=0.6)
ax.set_title("Estacions de tren a Europa")
plt.show()

import plotly.graph_objects as go
import math

path = kagglehub.dataset_download("atharvasoundankar/global-food-wastage-dataset-2018-2024")

print("Path to dataset files:", path)

Path to dataset files: C:\Users\esthe\.cache\kagglehub\datasets\atharvasoundankar\global-food-wastage-dataset-2018-2024\versions\1

df = pd.read_csv(path + "\\global_food_wastage_dataset.csv", encoding="utf8")
print(df.shape)
df.head()

(5000, 8)

# Agrupar per cada any i pais
df_yearly = df.groupby(["Country", "Year"], as_index=False)["Total Waste (Tons)"].sum()

# Mitjana per cada país.
df_avg = df_yearly.groupby("Country", as_index=False)["Total Waste (Tons)"].mean()
df_avg.rename(columns={"Total Waste (Tons)": "Total Waste (Tons) average"}, inplace=True)

print(df_avg.head())

     Country  Total Waste (Tons) average
0  Argentina               868909.645714
1  Australia               841051.612857
2     Brazil               913107.500000
3     Canada               970096.927143
4      China               915870.882857

ICON = "🥦"
UNIT = 25000    # 1 icon = 25000 tons

# Icones per pais
df_avg["ICONS"] = (df_avg["Total Waste (Tons) average"] / UNIT).apply(math.ceil)

# Llistes per cada pictograma
countries = []
icons = []
for _, row in df_avg.iterrows():
    for i in range(row["ICONS"]):
        countries.append(row["Country"])
        icons.append(ICON)

# Per cada pais
pictogram_df = pd.DataFrame({"Country": countries, "ICON": icons})
pictogram_df["y"] = pictogram_df.groupby("Country").cumcount()

# Pictograma
fig = go.Figure()

for country in pictogram_df["Country"].unique():
    subset = pictogram_df[pictogram_df["Country"] == country]
    fig.add_trace(go.Scatter(
        x=[country]*len(subset),
        y=subset["y"],
        mode="text",
        text=subset["ICON"],
        textfont=dict(size=20),
        hoverinfo="skip"
    ))

fig.update_layout(
    title="Menjar desaprofitat per país (🥦 = 25.000 Tonelades)",
    xaxis_title="Pais",
    yaxis_title="Menjar desaprofitat",
    showlegend=False,
    height=1000
)

fig.update_xaxes(tickangle=-45)

fig.show()

# Agrupar per cada any i pais
df_yearly = df.groupby(["Country", "Year"], as_index=False)["Avg Waste per Capita (Kg)"].sum()

# Mitjana per cada país.
df_avg = df_yearly.groupby("Country", as_index=False)["Avg Waste per Capita (Kg)"].mean()

print(df_avg.head())

     Country  Avg Waste per Capita (Kg)
0  Argentina                3733.375714
1  Australia                3501.554286
2     Brazil                3971.020000
3     Canada                4003.067143
4      China                4156.872857

ICON = "🥦"
UNIT = 150    # 1 icon = 150 Kg

# Icones per pais
df_avg["ICONS"] = (df_avg["Avg Waste per Capita (Kg)"] / UNIT).apply(math.ceil)

# Llistes per cada pictograma
countries = []
icons = []
for _, row in df_avg.iterrows():
    for i in range(row["ICONS"]):
        countries.append(row["Country"])
        icons.append(ICON)

# Per cada pais
pictogram_df = pd.DataFrame({"Country": countries, "ICON": icons})
pictogram_df["y"] = pictogram_df.groupby("Country").cumcount()

# Pictograma
fig = go.Figure()

for country in pictogram_df["Country"].unique():
    subset = pictogram_df[pictogram_df["Country"] == country]
    fig.add_trace(go.Scatter(
        x=[country]*len(subset),
        y=subset["y"],
        mode="text",
        text=subset["ICON"],
        textfont=dict(size=20),
        hoverinfo="skip"
    ))

fig.update_layout(
    title="Menjar desaprofitat per capita (🥦 = 150 Kg)",
    xaxis_title="Pais",
    yaxis_title="Menjar desaprofitat",
    showlegend=False,
    height=1000
)

fig.update_xaxes(tickangle=-45)

fig.show()

import plotly.express as px
import seaborn as sns

path = kagglehub.dataset_download("sujaykapadnis/fast-food-calories-data",)

print("Path to dataset files:", path)

Path to dataset files: C:\Users\esthe\.cache\kagglehub\datasets\sujaykapadnis\fast-food-calories-data\versions\1

df = pd.read_csv(path + "\\fastfood_calories.csv", encoding="utf8")
print(df.shape)
df.head()

(515, 18)

plt.figure(figsize=(12,6))
sns.swarmplot(
    data=df,
    x="restaurant",
    y="calories",
)
plt.title("Calories per producte del menú", fontsize=14)
plt.xlabel("Restaurant")
plt.ylabel("Calories")
plt.tight_layout()
plt.show()

# Obtenim les posicions de Seaborn
ax = sns.swarmplot(data=df, x="restaurant", y="calories")
swarm_positions = []

for collection in ax.collections:
    offsets = collection.get_offsets()
    swarm_positions.extend(offsets)

plt.close() 

# Dataframe amb les posicions
df_plot = df.copy()
df_plot["x_swarm"], df_plot["y_swarm"] = zip(*swarm_positions)

# Plotly scatter
fig = px.scatter(
    df_plot,
    x="x_swarm",
    y="y_swarm",
    hover_data=["item"],
    color="restaurant"
)

fig.update_layout(
    title="Calories per producte del menú (interactiu)",
    xaxis_title="Restaurant",
    yaxis_title="Calories",
    height=800, 
    legend=dict(
        orientation="h",
        yanchor="top",
        y=-0.2, 
        xanchor="center",
        x=0.5
    ),
    margin=dict(t=80, b=120) 
)

fig.show()

C:\Users\esthe\anaconda3\Lib\site-packages\seaborn\categorical.py:3399: UserWarning:

11.5% of the points cannot be placed; you may want to decrease the size of the markers or use stripplot.

C:\Users\esthe\anaconda3\Lib\site-packages\seaborn\categorical.py:3399: UserWarning:

29.6% of the points cannot be placed; you may want to decrease the size of the markers or use stripplot.

	id	name	name_norm	uic	latitude	longitude	parent_station_id	country	time_zone	is_city	is_main_station	is_airport	entur_id	entur_is_enabled
0	1	Château-Arnoux—St-Auban	Chateau-Arnoux-St-Auban	NA	44.08179	6.001625	NA	FR	Europe/Paris	TRUE	FALSE	FALSE	NA	FALSE
1	2	Château-Arnoux—St-Auban	Chateau-Arnoux-St-Auban	8775123	44.0615651	5.9973734	1	FR	Europe/Paris	FALSE	TRUE	FALSE	NA	FALSE
2	3	Château-Arnoux Mairie	Chateau-Arnoux Mairie	8775122	44.063863	6.011248	1	FR	Europe/Paris	FALSE	FALSE	FALSE	NA	FALSE
3	4	Digne-les-Bains	Digne-les-Bains	NA	44.35	6.35	NA	FR	Europe/Paris	TRUE	FALSE	FALSE	NA	FALSE
4	6	Digne-les-Bains	Digne-les-Bains	8775149	44.088710133980605	6.222982406616211	4	FR	Europe/Paris	FALSE	TRUE	FALSE	NA	FALSE

	Country	Year	Food Category	Total Waste (Tons)	Economic Loss (Million $)	Avg Waste per Capita (Kg)	Population (Million)	Household Waste (%)
0	Australia	2019	Fruits & Vegetables	19268.63	18686.68	72.69	87.59	53.64
1	Indonesia	2019	Prepared Food	3916.97	4394.48	192.52	1153.99	30.61
2	Germany	2022	Dairy Products	9700.16	8909.16	166.94	1006.11	48.08
3	France	2023	Fruits & Vegetables	46299.69	40551.22	120.19	953.05	31.91
4	France	2023	Beverages	33096.57	36980.82	104.74	1105.47	36.06

	Unnamed: 0	restaurant	item	calories	cal_fat	total_fat	sat_fat	trans_fat	cholesterol	sodium	total_carb	fiber	sugar	protein	vit_a	vit_c	calcium	salad
0	1	Mcdonalds	Artisan Grilled Chicken Sandwich	380	60	7	2.0	0.0	95	1110	44	3.0	11	37.0	4.0	20.0	20.0	Other
1	2	Mcdonalds	Single Bacon Smokehouse Burger	840	410	45	17.0	1.5	130	1580	62	2.0	18	46.0	6.0	20.0	20.0	Other
2	3	Mcdonalds	Double Bacon Smokehouse Burger	1130	600	67	27.0	3.0	220	1920	63	3.0	18	70.0	10.0	20.0	50.0	Other
3	4	Mcdonalds	Grilled Bacon Smokehouse Chicken Sandwich	750	280	31	10.0	0.5	155	1940	62	2.0	18	55.0	6.0	25.0	20.0	Other
4	5	Mcdonalds	Crispy Bacon Smokehouse Chicken Sandwich	920	410	45	12.0	0.5	120	1980	81	4.0	18	46.0	6.0	20.0	20.0	Other

Visualització de dades - PAC 2¶

Esther Ruano Hortoneda¶

Octubre 2025¶

Dot Density Map ¶

Origen i autoria:¶

Avantatges i inconvenients¶

Avantatges¶

Inconvenients¶

Tipus de dades admeses:¶

Exemples i aplicacions habituals¶

Visualització¶

Icon/Pictogram chart ¶

Origen i autoria:¶

Avantatges i inconvenients¶

Avantatges¶

Inconvenients¶

Tipus de dades admeses:¶

Exemples i aplicacions habituals¶

Visualització¶

Beeswarm plot ¶

Origen i autoria:¶

Avantatges i inconvenients¶

Avantatges¶

Inconvenients¶

Tipus de dades admeses:¶

Exemples i aplicacions habituals¶

Visualització¶

Visualització de dades - PAC 2¶

Esther Ruano Hortoneda¶

Octubre 2025¶

Dot Density Map¶

Origen i autoria:¶

Avantatges i inconvenients¶

Avantatges¶

Inconvenients¶

Tipus de dades admeses:¶

Exemples i aplicacions habituals¶

Visualització¶

Icon/Pictogram chart¶

Origen i autoria:¶

Avantatges i inconvenients¶

Avantatges¶

Inconvenients¶

Tipus de dades admeses:¶

Exemples i aplicacions habituals¶

Visualització¶

Beeswarm plot¶

Origen i autoria:¶

Avantatges i inconvenients¶

Avantatges¶

Inconvenients¶

Tipus de dades admeses:¶

Exemples i aplicacions habituals¶

Visualització¶

Dot Density Map ¶

Icon/Pictogram chart ¶

Beeswarm plot ¶